Weekly Exercises #4

library(tidyverse)     # for data cleaning and plotting
library(lubridate)     # for date manipulation
library(openintro)     # for the abbr2state() function
library(palmerpenguins)# for Palmer penguin data
library(maps)          # for map data
library(ggmap)         # for mapping points on maps
library(gplots)        # for col2hex() function
library(RColorBrewer)  # for color palettes
library(sf)            # for working with spatial data
library(leaflet)       # for highly customizable mapping
library(carData)       # for Minneapolis police stops data
library(ggthemes)      # for more themes (including theme_map())

# Starbucks locations
Starbucks <- read_csv("https://www.macalester.edu/~ajohns24/Data/Starbucks.csv")

starbucks_us_by_state <- Starbucks %>% 
  filter(Country == "US") %>% 
  count(`State/Province`) %>% 
  mutate(state_name = str_to_lower(abbr2state(`State/Province`))) 


#COVID-19 data from the New York Times
covid19 <- read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv")

Starbucks locations (ggmap)

1. Add the Starbucks locations to a world map. Add an aesthetic to the world map that sets the color of the points according to the ownership type. What, if anything, can you deduce from this visualization? We can deduce that most of the Starbucks locations are found in North America and are licensed or company owned. Joint ventures are mostly only found in East Asia and some of Northern Europe and then the rest of the Starbucks are licensed

world <- get_stamenmap(
    bbox = c(left = -180, bottom = -57, right = 179, top = 82.1), 
    maptype = "terrain",
    zoom = 2)

ggmap(world) + 
  geom_point(data = Starbucks, 
             aes(x = Longitude, y = Latitude, color = `Ownership Type`), 
             alpha = .3, 
             size = .5) +
  labs(title = "Locations of Starbucks all over ther world",
       caption = "Author : Chloe Nance",
       fill = "") +
  theme_map() +
  theme(plot.title = element_text(face = "bold", hjust = .5)) 

2. Construct a new map of Starbucks locations in the Twin Cities metro area (approximately the 5 county metro area).

twin_cities <- get_stamenmap(
  bbox = c(left = -93.7206, bottom = 44.6813, right = -92.7202, top = 45.2837),
  maptype = "terrain",
  zoom = 9)

ggmap(twin_cities) +
  geom_point(data = Starbucks,
             aes(x = Longitude, y = Latitude)) +
    labs(title = "Locations of Starbucks in the Twin Cities",
       caption = "Author : Chloe Nance",
       fill = "") +
  theme_map() +
  theme(plot.title = element_text(face = "bold", hjust = .5)) 

3. In the Twin Cities plot, play with the zoom number. What does it do? (just describe what it does - don’t actually include more than one map). The lower the zoom number, the more zoomed in the map is. For example between 1-5 it is really zoomed in and we can’t tell what we are looking at, we just see blurred/pixelated image with black dots. But when the number is bigger (9 and up), we clearly see with labels where we are.

4. Try a couple different map types (see get_stamenmap() in help and look at maptype). Include a map with one of the other map types.

twin_cities <- get_stamenmap(
  bbox = c(left = -93.7206, bottom = 44.6813, right = -92.7202, top = 45.2837),
  maptype = "watercolor",
  zoom = 10)

ggmap(twin_cities) +
  geom_point(data = Starbucks,
             aes(x = Longitude, y = Latitude)) +
    labs(title = "Locations of Starbucks in the Twin Cities",
       caption = "Author : Chloe Nance",
       fill = "") +
  theme_map() +
  theme(plot.title = element_text(face = "bold", hjust = .5)) 

5. Add a point to the map that indicates Macalester College and label it appropriately. There are many ways you can do think, but I think it’s easiest with the annotate() function (see ggplot2 cheatsheet).

twin_cities <- get_stamenmap(
  bbox = c(left = -93.7206, bottom = 44.6813, right = -92.7202, top = 45.2837),
  maptype = "terrain",
  zoom = 10)

ggmap(twin_cities) +
  geom_point(data = Starbucks,
             aes(x = Longitude, y = Latitude)) + 
   annotate("point",
           x = -93.17123,
           y = 44.93790,
           color = "red") +
   annotate("text",
           x = -93.17123,
           y = 44.93,
           color = "red",
           label = "Macalester") +
    labs(title = "Locations of Starbucks in the Twin Cities",
         subtitle = "In relation to Macalester College",
       caption = "Author : Chloe Nance",
       fill = "") +
  theme_map() +
  theme(plot.title = element_text(face = "bold", hjust = .5),
        plot.subtitle = element_text(face = "bold", hjust = .5)) 

Choropleth maps with Starbucks data (geom_map())

The example I showed in the tutorial did not account for population of each state in the map. In the code below, a new variable is created, starbucks_per_10000, that gives the number of Starbucks per 10,000 people. It is in the starbucks_with_2018_pop_est dataset.

census_pop_est_2018 <- read_csv("https://www.dropbox.com/s/6txwv3b4ng7pepe/us_census_2018_state_pop_est.csv?dl=1") %>% 
  separate(state, into = c("dot","state"), extra = "merge") %>% 
  select(-dot) %>% 
  mutate(state = str_to_lower(state))

starbucks_with_2018_pop_est <-
  starbucks_us_by_state %>% 
  left_join(census_pop_est_2018,
            by = c("state_name" = "state")) %>% 
  mutate(starbucks_per_10000 = (n/est_pop_2018)*10000) 

6. dplyr review: Look through the code above and describe what each line of code does. line 169 : giving census_pop_est_2018 data to R line 170 : separating the “state” column into two separate columns (“dot” and “state”) of equal length to the original dataset. line 171 : selecting to “dots” column to remove it from the new dataset. line 172 : taking the “state” variable and making the names all lowercase. line 174 : Naming this new dataset starbucks_with_2018_pop_es. line 175 : piping in starbucks_us_by_state dataset to create this new one named above. line 176/177 : joining the starbucks_us_by_state and the census_pop_est_2018 by “state_name” which is equivlen to “state” in the census dataset. line 178 : creating a new variable, starbucks_per_10000, which is equivalent to the number of rows divided by the est_pop_2018 variable, times 10,000.

7. Create a choropleth map that shows the number of Starbucks per 10,000 people on a map of the US. Use a new fill color, add points for all Starbucks in the US (except Hawaii and Alaska), add an informative title for the plot, and include a caption that says who created the plot (you!). Make a conclusion about what you observe. It seems the most amount of starbucks are on the west coast, near where they originated, while smaller/more rural states such as Oklahoma, Arkansas, North Dakota, etc, have the least amount, and the rest of the states are in an average range.

states_map <- map_data("state")

starbucks_with_2018_pop_est %>% 
  ggplot() +
  geom_map(map = states_map,
           aes(map_id = state_name,
               fill = (n/est_pop_2018)*10000)) +
  expand_limits(x = states_map$long, y = states_map$lat) + 
  labs(title = "Quantity Starbucks in the US per 10,000",
       caption = "Author : Chloe Nance",
       fill = "") +
  scale_fill_viridis_c(option = "inferno") +
   labs(title = "Number of Starbucks per 10,00 people in the US",
       caption = "Author : Chloe Nance",
       fill = "") +
  theme_map() +
  theme(plot.title = element_text(face = "bold", hjust = .5),
        legend.background = element_blank())

A few of your favorite things (leaflet)

8. In this exercise, you are going to create a single map of some of your favorite places! The end result will be one map that satisfies the criteria below.

• Create a data set using the tibble() function that has 10-15 rows of your favorite places. The columns will be the name of the location, the latitude, the longitude, and a column that indicates if it is in your top 3 favorite locations or not. For an example of how to use tibble(), look at the favorite_stp_by_lisa I created in the data R code chunk at the beginning.

• Create a leaflet map that uses circles to indicate your favorite places. Label them with the name of the place. Choose the base map you like best. Color your 3 favorite places differently than the ones that are not in your top 3 (HINT: colorFactor()). Add a legend that explains what the colors mean.

• Connect all your locations together with a line in a meaningful way (you may need to order them differently in the original data).

• If there are other variables you want to add that could enhance your plot, do that now.

favorite_plcs_chloe <- tibble(
  place = c("Home", "Hmart", "Clair's", 
            "Dong Chun Hong", "Super Chix", "Uchi", 
            "La Madeleine", "Cauldron Ice Cream", "Gold dust Tattoo", "Fatstraws" ),
  long = c(-96.759950,-96.698882, -96.8093864,
           -96.9125064, -96.8045697, -96.8065458,
           -96.8036841, -96.7896389,  -96.7701521, -96.8034734),
  lat = c(32.911510, 33.040037, 32.9173171,
          32.9846813, 32.9529158, 32.7968572,
          32.9116192, 32.8234591, 32.830864, 32.9100582),
  top_three = c("yes", "yes", "yes", "no", "no", "no", "no", "no", "no","no")
  )

pal <- colorFactor("viridis", domain = favorite_plcs_chloe$top_three)

leaflet(favorite_plcs_chloe) %>% 
  addProviderTiles(providers$CartoDB.DarkMatter) %>% 
  addCircles(lng = ~long,
             lat = ~lat,
             label = ~place,
             color = ~pal(top_three),
             opacity = 5,
             weight = 15) %>% 
  addPolylines(lng = ~long, 
               lat = ~lat, 
               color = col2hex("red"),
               opacity = .3) %>% 
  addLegend(pal = pal, values = ~top_three)

Revisiting old datasets

This section will revisit some datasets we have used previously and bring in a mapping component.

Bicycle-Use Patterns

The data come from Washington, DC and cover the last quarter of 2014.

Two data tables are available:

• Trips contains records of individual rentals
• Stations gives the locations of the bike rental stations

Here is the code to read in the data. We do this a little differently than usualy, which is why it is included here rather than at the top of this file. To avoid repeatedly re-reading the files, start the data import chunk with {r cache = TRUE} rather than the usual {r}. This code reads in the large dataset right away.

data_site <- 
  "https://www.macalester.edu/~dshuman1/data/112/2014-Q4-Trips-History-Data.rds" 
Trips <- readRDS(gzcon(url(data_site)))
Stations<-read_csv("http://www.macalester.edu/~dshuman1/data/112/DC-Stations.csv")

9. Use the latitude and longitude variables in Stations to make a visualization of the total number of departures from each station in the Trips data. Use either color or size to show the variation in number of departures. This time, plot the points on top of a map. Use any of the mapping tools you’d like.

USA_map <- get_stamenmap(
  bbox = c(left = -77.4117, bottom = 38.7094, right = -76.6757, top = 39.0761),
  maptype = "terrain",
  zoom = 11)

Station_Trips <- Trips %>% 
  mutate(name = sstation) %>% 
  left_join(Stations,
            by = "name") %>% 
  group_by(lat, long) %>% 
  summarise(freq_stations = n())

ggmap(USA_map) +
  geom_point(data = Station_Trips,
             aes(x = long, y = lat, color = freq_stations),
             alpha = 1,
             size = 1) +
  labs(title = "Total number of departures per station",
       caption = "Author : Chloe Nance",
       fill = "") +
  theme_map() +
  theme(plot.title = element_text(face = "bold", hjust = .5))

10. Only 14.4% of the trips in our data are carried out by casual users. Create a plot that shows which area(s) have stations with a much higher percentage of departures by casual users. What patterns do you notice? Also plot this on top of a map. I think it will be more clear what the patterns are. It seems the most casual riders are located in the center of the city which makes sense as it would be an easy form of transportation to get around while the registered users may want to venture out further away from the city

USA_map <- get_stamenmap(
  bbox = c(left = -77.4117, bottom = 38.7094, right = -76.6757, top = 39.0761),
  maptype = "terrain",
  zoom = 10)

departures <- Trips %>% 
  mutate(name = sstation) %>% 
  left_join(Stations,
            by = c("name")) %>% 
  group_by(lat, long) %>% 
  summarise(prop_casual = sum(client == "Casual")/n())
  

ggmap(USA_map) +
  geom_point(data = departures,
             aes(x = long, y = lat, color = prop_casual),
             alpha = 1,
             size = 1) +
  scale_colour_viridis_c() +
  labs(title = "Percentage of departures by casual users",
       caption = "Author : Chloe Nance",
       fill = "") +
  theme_map() +
  theme(plot.title = element_text(face = "bold", hjust = .5))

COVID-19 data

The following exercises will use the COVID-19 data from the NYT.

11. Create a map that colors the states by the most recent cumulative number of COVID-19 cases (remember, these data report cumulative numbers so you don’t need to compute that). Describe what you see. What is the problem with this map? It seems that only a few states have the highest amount of cumulative cases (Texas, Cali, and Florida) while the rest are relatively lower. The problem seems to be that it doesn’t show precicely enoug the amount of casses because even though they are relatively low compared to each other, compared to the world it is still high

states_map <- map_data("state")

covid19 %>% 
  group_by(state) %>% 
  summarise(sum_covid = max(cases)) %>% 
  mutate(states = str_to_lower(state)) %>% 
  ggplot() +
  geom_map(map = states_map,
           aes(map_id = states,
               fill = sum_covid)) +
  expand_limits(x = states_map$long, y = states_map$lat) + 
   scale_fill_viridis_c(option = "inferno") +
  labs(title = "Most recent cumulative COVID-19 cases per state",
       caption = "Author : Chloe Nance",
       fill = "") +
  theme_map() +
  theme(plot.title = element_text(face = "bold", hjust = .5),
        legend.background = element_blank())

12. Now add the population of each state to the dataset and color the states by most recent cumulative cases/10,000 people. See the code for doing this with the Starbucks data. You will need to make some modifications.

covid_10000 <-
  covid19 %>% 
  group_by(state) %>% 
  summarise(covid_sum = max(cases)) %>% 
  mutate(states = str_to_lower(state)) %>% 
  left_join(census_pop_est_2018, by = c("states" = "state")) %>% 
  mutate(cases_per10 = (covid_sum/est_pop_2018)*10000)

states_map <- map_data("state")

covid_10000 %>% 
  ggplot() +
  geom_map(map = states_map,
           aes(map_id = states,
               fill = cases_per10)) +
  expand_limits(x = states_map$long, y = states_map$lat) + 
   scale_fill_viridis_c(option = "inferno") +
  labs(title = "Most recent cumulative COVID-19 cases per state",
       caption = "Author : Chloe Nance",
       fill = "") +
  theme_map() +
  theme(plot.title = element_text(face = "bold", hjust = .5),
        legend.background = element_blank())

13. CHALLENGE Choose 4 dates spread over the time period of the data and create the same map as in exercise 12 for each of the dates. Display the four graphs together using faceting. What do you notice?

Minneapolis police stops

These exercises use the datasets MplsStops and MplsDemo from the carData library. Search for them in Help to find out more information.

14. Use the MplsStops dataset to find out how many stops there were for each neighborhood and the proportion of stops that were for a suspicious vehicle or person. Sort the results from most to least number of stops. Save this as a dataset called mpls_suspicious and display the table.

mpls_suspicious <- MplsStops %>% 
  group_by(neighborhood) %>% 
  summarise(freq_stops = n(),
            prop_sus = sum(problem == "suspicious")/n()) %>% 
  arrange(freq_stops)

mpls_suspicious

15. Use a leaflet map and the MplsStops dataset to display each of the stops on a map as a small point. Color the points differently depending on whether they were for suspicious vehicle/person or a traffic stop (the problem variable). HINTS: use addCircleMarkers, set stroke = FAlSE, use colorFactor() to create a palette.

pal <- colorFactor("viridis", domain = MplsStops$problem)

leaflet(MplsStops) %>% 
  addProviderTiles(providers$CartoDB.DarkMatter) %>% 
  addCircles(lng = ~long,
             lat = ~lat,
             color = ~pal(problem),
             opacity = 5,
             weight = 15,
             stroke = FALSE) %>% 
  addLegend(pal = pal, values = ~problem)

16. Save the folder from moodle called Minneapolis_Neighborhoods into your project/repository folder for this assignment. Make sure the folder is called Minneapolis_Neighborhoods. Use the code below to read in the data and make sure to delete the eval=FALSE. Although it looks like it only links to the .sph file, you need the entire folder of files to create the mpls_nbhd data set. These data contain information about the geometries of the Minneapolis neighborhoods. Using the mpls_nbhd dataset as the base file, join the mpls_suspicious and MplsDemo datasets to it by neighborhood (careful, they are named different things in the different files). Call this new dataset mpls_all.

mpls_nbhd <- st_read("Minneapolis_Neighborhoods/Minneapolis_Neighborhoods.shp", quiet = TRUE)

mpls_all <- mpls_nbhd %>% 
  mutate(neighborhood = BDNAME) %>% 
  left_join(mpls_suspicious,
            MplsDemo,
            by = c("neighborhood"))

17. Use leaflet to create a map from the mpls_all data that colors the neighborhoods by prop_suspicious. Display the neighborhood name as you scroll over it. Describe what you observe in the map. It seems that the most suspicious activity happens in the neighborhoods in the south east while the least amount of suspicious activity is in the north.

pal <- colorNumeric("viridis", domain = mpls_all$prop_sus)

leaflet(mpls_all) %>% 
  addProviderTiles(providers$CartoDB.DarkMatter) %>% 
  addPolygons(label = ~neighborhood,
             fillColor = ~pal(prop_sus),
             color = ~pal(prop_sus),
             opacity = 5,
             fillOpacity = .8) %>%
  addLegend(pal = pal, values = ~prop_sus)

18. Use leaflet to create a map of your own choosing. Come up with a question you want to try to answer and use the map to help answer that question. Describe what your map shows. This map shows proportion of poverty in each Minneapolis neighborhood.

mpls_demo_nbhd <- mpls_nbhd %>% 
  mutate(neighborhood = BDNAME) %>% 
  left_join(MplsDemo,
            by = c("neighborhood")) 

pal <- colorNumeric("viridis", domain = mpls_demo_nbhd$poverty)

leaflet(mpls_demo_nbhd) %>% 
  addProviderTiles(providers$CartoDB.DarkMatter) %>%
  addPolygons(label = ~neighborhood,
             fillColor = ~pal(poverty),
             color = ~pal(poverty),
             opacity = 5,
             fillOpacity = .8) %>%
  addLegend(pal = pal, values = ~poverty)

GitHub link

19. Below, provide a link to your GitHub page with this set of Weekly Exercises. Specifically, if the name of the file is 04_exercises.Rmd, provide a link to the 04_exercises.md file, which is the one that will be most readable on GitHub.

DID YOU REMEMBER TO UNCOMMENT THE OPTIONS AT THE TOP?